Supply Chain Management in Manufacturing and Service Systems: Advanced Analytics for Smarter Decisions (International Series in Operations Research & Management Science, 304)

"Management of supply chains has been evolving rapidly over the last few years due to the inception of Industry 4.0, where businesses adopt automation technologies and data exchanges leading to dynamic and interconnected supply chain systems. Emphasizing on analytical approaches such as predictive and prescriptive modeling, this book presents state-of-the-art original research work dealing with advanced analytical models for the design, planning, and operation of the supply chain to provide faster and smarter decisions in the era of digitization. In particular, the book integrates machine learning and operations research models for faster and smarter decisions, presents prescriptive analytics models for strategic, tactical, and operational decision making in the supply chain, and addresses recent challenges such as sustainability in the supply chain, supply chain visibility, and supply chain digitalization. Key concepts are illustrated using real-life case studies, making the book a valuable reference for researchers, technical professionals, and students."--Page [4] of cover Preface Book Overview Chapter Summaries Acknowledgments Contents Contributors An Overview of Decisions, Performance and Analytics in Supply Chain Management 1 Overview of Supply Chain 2 Supply Chain Decision Levels 2.1 Strategic 2.2 Tactical 2.3 Operational 3 Supply Chain Enablers and Drivers 4 Types of Supply Chain 4.1 Responsive Supply Chain 4.2 Efficient Supply Chain 4.3 Resilient Supply Chain 4.4 Humanitarian Supply Chain 4.5 Green Supply Chain 4.6 Sustainable Supply Chain 5 Impact of Industry 4.0 on Supply Chain References Intelligent Digital Supply Chains 1 Introduction 2 Digital Supply Chains 2.1 Challenges in Digital Supply Chain 2.2 Business Processes Evolution and Trends in Digital Supply Chain 2.3 New Business Models Enabled by Digital Supply Chain 2.4 Design-to-Operate Business Process 3 Intelligent Visibility in Supply Chain Networks 3.1 Challenges Achieving End-to-End Visibility 3.2 Benefits of Having Visibility in Digital Supply Chain Include 4 Global and Local Control Towers Providing Global E2E Visibility 5 Next-Generation Supply Chain Analytics 5.1 Common Analytical Charts Relevant for Supply Chain 5.2 Business User Friendly Self-Service Analytics 6 Supply Chain Alerts and Exception Management 7 Insight to Action 8 Supply Chain Key Performance Indicators 9 Cognitive Supply Chains Enabled by Technologies in Industry 4.0 10 Resilient Supply Chains 11 Collaborative Enterprise Planning: Integrated Supply Chain and Financial Planning References Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design 1 Introduction 2 Literature Review 2.1 Product Life Cycle Dynamics 2.2 Consumer Perception of Remanufactured Products 2.3 Demand Cannibalization 2.4 Pricing Under Remanufacturing 3 Methodology 3.1 Product Life Cycle Optimization Model for CLSC 3.2 Product Life Cycle Optimization Model (PLCOM) for CLSC 3.3 Assumptions of the Integrated Optimization Model 3.4 Optimization Model 3.5 Case Study Applying PLCOM to Design Supply Chain Network for iPhone Problem Description Problem Size Solution and discussion 3.6 Illustration of Profit Reduction due to CLSC Network Design in Sequential Manner 3.7 Sensitivity Analysis Optimal Time Period to Introduce Remanufactured Product in the Market Impact on Profits Impact on Shortages Impact on New and Remanufactured Product Sales Impact on First-Time and Repeat Sales 3.8 Sensitivity Analysis Impact on the Supply Chain Network 3.9 Characterization of Slow and Fast Diffusing Products Impact on Profit and Network Design Impact on New and Remanufactured Product Sales Impact on First-Time Sales, Repeat Sales and Shortage 3.10 Incentivizing Consumers to Improve the Quality of Returns 4 Conclusions Appendices Appendix 1 Pricing Model Appendix 2 Demand Model Demand Equations Appendix 3 Integration of Pricing and Demand Models Appendix 4 References Supply Chain Risk Management in Indian Manufacturing Industries: An Empirical Study and a Fuzzy Approach 1 Background and Motivation 2 Literature Review 2.1 Critical Factors for SCRM Description of the Construct of SCRM: Manufacturer's Perspective Conceptual Framework 2.2 Fuzzy Cognitive Map (FCM) and Its Applications in the Present Study: An Overview Risk Modeling: Graph Theory Approach Basics of Fuzzy Cognitive Maps The Proposed Model: Mapping Supply Chain Risks and Mitigation Strategies Construction of FCM Prediction of Future Risks Based on the Current State of Risk Observance Identification and Effectiveness of Mitigation Strategy for Risk During the Run 2.3 Fuzzy TOPSIS Approach 2.4 Research Gaps, Hypotheses and Contributions 3 Methods 3.1 Data Description 3.2 Survey Description 3.3 Scales Used to Measure the Latent Variables 3.4 Construction of FCM for SCRM 3.5 Statistical Analysis Empirical Validation of the Proposed SCRM Constructs Reliability and Validity Tests 4 Results 4.1 Factor Loadings 4.2 Sobel Test for Mediation 4.3 Bivariate Correlation Between the Constructs 4.4 FCM Results 4.5 Fuzzy TOPSIS Approach 5 Discussion (Managerial and Theoretical Implications) 5.1 Validation of the Proposed Conceptual Framework Inference from the Empirical Study Implications for Practitioners Suggestions and Recommendations for Policy Makers 5.2 Results of Application of FCM to SCRM 5.3 Comparison of the Proposed FCM and Fuzzy TOPSIS Model 6 Conclusions References Improving Service Supply Chain of Internet Services by Analyzing Online Customer Reviews 1 Introduction 2 Literature Review 2.1 Wireless Service Providers 2.2 Electronic Word of Mouth (eWOM) 3 Methodology 3.1 Web Scraping and Text Pre-Processing 3.2 Topic Identification Using Bigrams and Trigrams Analyses 3.3 SWOT Strategic Planning Using Bigrams and Trigrams Analyses 4 Case Study 4.1 Data Description 4.2 Experimental Results 4.3 SWOT Analysis 4.4 Link and Root Cause Analyses 4.5 Managerial Implications 5 Conclusions References An Integrated Problem of Production Scheduling and Transportation in a Two-Stage Supply Chain with Carbon Emission Consideration 1 Introduction 2 Literature Review 3 Problem Description 4 Development of the Proposed Mixed Integer Linear Programming Model 5 A Numerical Example 6 Computational Experimentation 7 Summary References A Simulation-Based Evaluation of Drone Integrated Delivery Strategies for Improving Pharmaceutical Service 1 Introduction 2 Literature Review 3 Problem Statement 4 Methodology 4.1 System Description 4.2 Sequence of Events 4.3 Data Collection and Analysis 4.4 Model Parameters 5 Discrete Event Simulation 5.1 Verification and Validation Validation 6 Alternative Scenarios 6.1 Scenario 1: Truck-Only 6.2 Scenario 2: Truck-Tandem 6.3 Scenario 3: Drone-Only 7 Results 7.1 Scenario 1: Truck-Only 7.2 Scenario 2: Truck–Tandem 7.3 Scenario 3: Drone-Only 7.4 Comparable Results 7.5 Sensitivity Analysis 7.6 Discussion 7.7 Managerial Implications 8 Conclusion and Future Work References Pro-Active Strategies in Online Routing 1 Introduction 2 A Reactive Real-Time Approach 3 Exploiting Past Request Data and Building the Stochastic Knowledge 3.1 Derivation of Stochastic Knowledge by Applying a Specifically Designed Cluster Analysis 3.2 Ensuring Two Further Cluster Quality Criteria 3.3 Selection of the Clusters 4 Transforming the Reactive Real-Time Approach into a Pro-active One 5 Computational Evaluation 5.1 Generating the Instances of the Data Class SGEN 5.2 Measured Results for the Instances of the Data Class SREAL 5.3 Measured Results for the Instances of the Data Class SGEN 6 Efficiently Controlling en Route Diversions 7 Identifying Multiple Profiles in the Past Request Data 8 Brief Summary References Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces 1 Introduction 2 Problem Description and Assumption 3 Related Literature Review 4 Mathematical Model for Dynamic Scheduling of Diffusion Furnaces 4.1 (0-1) MILP Model for DS-SDF 4.2 (0-1) MILP Model for DS-NPDF-MER 5 ATC Based GHA for Scheduling Diffusion Furnaces 5.1 ATC-GHA for DRTS of SDF 5.2 ATC-GHA for DRTS for NPDF-MER 6 Performance Evaluation of ATC-GHA for DRTS of Diffusion Furnaces 6.1 Empirical Analyses on the Performance of ATC-GHA for DRTS of Diffusion Furnaces 6.2 Statistical Analyses on the Performance of ATC-GHA for DRTS of Diffusion Furnaces 7 Conclusion References